Innovative Image Processing Methods for Lung Cancer Detection and Stage Prediction

The persistent threat that lung cancer poses to people's health emphasizes the urgent requirement for cuttingedge detection technology. This work offers a novel approach for diagnosing and staging lung cancer using modern image processing tools. Working with radiologists, we carefully selected regions of interest in a broad sample of 1000 CT scan images. Significant morphological traits of lung nodules were found using feature extraction. Our model, a hybrid CNN-RNN architecture, used deep learning to handle this rich feature data. The results were excellent, and our model's remarkable 96 % accuracy rate was one of them. According to research, the model performs better than other well-known techniques like CNN, SVM, DNN, RNN, Naive Bayes, and Random Forest. By fundamentally altering early lung cancer diagnosis, these results hold out hope for better patient care and outcomes. By improving lung cancer detection and prediction, this study significantly advances medical image processing.